Blood flow dynamic characteristic based on fractional flow reserve (FFR) has been known as a reliable parameter for determining and evaluating the optimal treatment plan of the patients with diseased arteries. Clinical trials show that FFR may be used guide treatments of coronary artery stenosis and other vascular diseases. For example, for cardiovascular diseases, if FFR value is larger than 0.8, drug treatment may be selected. Otherwise, intervention treatment may be adopted. Various blood flow features, including FFR, may provide important reference for the physician during cardiovascular diagnosis.
Invasive quantitative measurement remains the clinical gold standard to assessment of the vascular diseases of the human body. Although attempts to introduce non-invasive methods are made to estimate the blood flow features and diagnose vascular diseases of the human body, it is difficult for these non-invasive methods to be implemented in the clinical environment due to computational complexity, the lengthy time consumption, and inaccurate estimation results.
For example, since a majority of the target objects have complicated vessel paths and vessel tree structures, the existing non-invasive methods can not accurately predict the blood flow features such as FFR. Especially, the vessel tree typically includes a large number of vessel paths due to vessel bifurcations or turning, which further complicates the prediction of blood flow features.
The present disclosure provides an improved system and method for automatically predicting a blood flow feature based on a medical image.